Ammonia: an Approach for Deriving Project-Specific Bug PatternsJournal-First
Thu 27 May 2021 22:20 - 22:40 at Blended Sessions Room 3 - 3.1.3. Defect Prediction: Automation #2
Finding and fixing buggy code is an important and cost-intensive maintenance task, and static analysis (SA) is one of the methods developers use to perform it. SA tools warn developers about potential bugs by scanning their source code for commonly occurring bug patterns, thus giving those developers opportunities to fix the warnings (potential bugs) before they release the software. Typically, SA tools scan for general bug patterns that are common to any software project (such as null pointer dereference), and not for project-specific patterns. However, past research has pointed to this lack of customizability as a severe limiting issue in SA. Accordingly, in this paper, we propose an approach called Ammonia, which is based on statically analyzing changes across the development history of a project, as a means to identify project-specific bug patterns. Furthermore, the bug patterns identified by our tool do not relate to just one developer or one specific commit, they reflect the project as a whole and complement the warnings from other SA tools that identify general bug patterns. Herein, we report on the application of our implemented tool and approach to four Java projects: Ant, Camel, POI, and Wicket. The results obtained show that our tool could detect 19 project-specific bug patterns across those four projects. Next, through manual analysis, we determined that six of those change patterns were actual bugs and submitted pull requests based on those bug patterns. As a result, five of the pull requests were merged.
Thu 27 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:00 - 11:00 | 3.1.3. Defect Prediction: Automation #2Journal-First Papers at Blended Sessions Room 3 +12h Chair(s): Robert Feldt Chalmers | University of Gothenburg, Blekinge Institute of Technology | ||
10:00 20mPaper | Revisiting Supervised and Unsupervised Methods for Effort-Aware Cross-Project Defect PredictionJournal-First Journal-First Papers Chao Ni Zhejiang University, Xin Xia Huawei Software Engineering Application Technology Lab, David Lo Singapore Management University, Xiang Chen Nantong University, Qing Gu Nanjing University Pre-print Media Attached | ||
10:20 20mPaper | Ammonia: an Approach for Deriving Project-Specific Bug PatternsJournal-First Journal-First Papers Yoshiki Higo Osaka University, Shinpei Hayashi Tokyo Institute of Technology, Hideaki Hata Shinshu University, Mei Nagappan University of Waterloo Link to publication DOI Authorizer link Pre-print Media Attached | ||
10:40 20mPaper | Predicting Defective Lines Using a Model-Agnostic TechniqueJournal-First Journal-First Papers Supatsara Wattanakriengkrai Nara Institute of Science and Technology, Patanamon Thongtanunam University of Melbourne, Kla Tantithamthavorn Monash University, Hideaki Hata Shinshu University, Kenichi Matsumoto Nara Institute of Science and Technology DOI Pre-print Media Attached |